Dynamic pricing is a widely applied strategy by ride-hailing companies, such as Uber and Lyft, to match the trip demand with the availability of drivers. Deciding proper pricing policies is challenging and existing reinforcement learning (RL)-based solutions are restricted in solving small-scale problems.
We study dynamic pricing policies for ridesharing platforms such as Lyft and Uber. On one hand these platforms are two-sided: this requires economic models ...
We show using data from Uber that by jointly optimizing DP and DW, price variability can be mitigated, while increasing capacity utilization, trip throughput, ...
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2.2. To model dynamic pricing, we allow the platform to choose prices that can vary based on the number of available drivers A. Formally, a pricing policy for ...
We study dynamic pricing policies for ridesharing platforms such as Lyft and Uber. On one hand these platforms are two-sided: this requires economic models ...
Duration: 1:04:54
Posted: May 1, 2018
Posted: May 1, 2018
Nov 14, 2023 · Ridesharing platforms match riders and drivers, using dynamic pricing to balance supply and demand. The origin-based "surge pricing", however, ...
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Oct 1, 2018 · We first review matching and dynamic pricing techniques in ride-hailing, and show that these are critical for providing an experience with low waiting time.
Missing: ridesharing | Show results with:ridesharing
May 9, 2023 · Professor David Brown and co-authors developed a dynamic pricing model for spatially distributed demand-based services, such as ride sharing.
Aug 10, 2021 · The main purpose of this paper is to establish the optimal pricing model of ridesharing platforms to dynamically coordinate uncertain supply and stochastic ...